{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7lZ4NLAZ+DQwgpoherGK+RESkhtoMBulOpQCY2dHuvlfu7TPN7EniamQREenlip5AXm1m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\nIigYiIgICgYiIoKCgYiIoGAgIiLA/wdvpq48GRomaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f50cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.cluster.hierarchy as sch\n",
    "import matplotlib.pyplot as plt\n",
    "dendrogram = sch.dendrogram(sch.linkage(iris.data, method = 'ward'))\n",
    "plt.title('Dendrogram')\n",
    "plt.xlabel('Iris')\n",
    "plt.ylabel('Euclidean distances')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 0,\n",
       "       2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2,\n",
       "       0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "hc = AgglomerativeClustering(n_clusters = 3, affinity = 'euclidean', linkage = 'ward')\n",
    "y_hc = hc.fit_predict(iris.data)\n",
    "\n",
    "y_hc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fqwwrbFAIPSGeu68ys2kE+QqjgM+7+9ZCrlJEqkrYmgZLlgw/Zr+1NVifqc2l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\npP8PZwDKQu7Qf8YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa42b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(iris.data[y_hc == 0, 2], iris.data[y_hc == 0, 3], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(iris.data[y_hc == 1, 2], iris.data[y_hc == 1, 3], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(iris.data[y_hc == 2, 2], iris.data[y_hc == 2, 3], s = 100, c = 'green', label = 'Cluster 3')\n",
    "\n",
    "plt.title('Clusters of Iris')\n",
    "plt.xlabel('Petal.Length')\n",
    "plt.ylabel('Petal.Width')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Iruv/imAm0VTgCndfXrbo0tCUVKmWMJXF+fQqGKhonj07OEMYqDCePr088Yc1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\nREQk6f8B7sg7n7hiNiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f979b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(iris.data[iris.target == 0, 2], iris.data[iris.target == 0, 3], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(iris.data[iris.target == 1, 2], iris.data[iris.target == 1, 3], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(iris.data[iris.target == 2, 2], iris.data[iris.target == 2, 3], s = 100, c = 'green', label = 'Cluster 3')\n",
    "\n",
    "plt.title('Clusters of Iris')\n",
    "plt.xlabel('Petal.Length')\n",
    "plt.ylabel('Petal.Width')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
